Overview

Dataset statistics

Number of variables21
Number of observations155159
Missing cells1058290
Missing cells (%)32.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.9 MiB
Average record size in memory168.0 B

Variable types

NUM14
CAT6
DATE1

Warnings

wind_spd has a high cardinality: 2124 distinct values High cardinality
pct_possible has a high cardinality: 102 distinct values High cardinality
wind_chill is highly correlated with tempHigh correlation
temp is highly correlated with wind_chillHigh correlation
sta_press is highly correlated with sea_lvl_press and 1 other fieldsHigh correlation
sea_lvl_press is highly correlated with sta_press and 1 other fieldsHigh correlation
altimeter_setting is highly correlated with sea_lvl_press and 1 other fieldsHigh correlation
file_name is highly correlated with site and 1 other fieldsHigh correlation
site is highly correlated with file_nameHigh correlation
year is highly correlated with file_nameHigh correlation
dew_pt has 18320 (11.8%) missing values Missing
rH has 18376 (11.8%) missing values Missing
heat_idx has 152201 (98.1%) missing values Missing
wind_chill has 96191 (62.0%) missing values Missing
wind_dir has 7972 (5.1%) missing values Missing
hr_precip has 120618 (77.7%) missing values Missing
snow_depth has 68084 (43.9%) missing values Missing
snowfall_3hr has 69198 (44.6%) missing values Missing
snowfall_6hr has 69547 (44.8%) missing values Missing
snowfall_24hr has 69801 (45.0%) missing values Missing
sea_lvl_press has 87974 (56.7%) missing values Missing
sta_press has 70210 (45.3%) missing values Missing
altimeter_setting has 70210 (45.3%) missing values Missing
solar_radiation has 69794 (45.0%) missing values Missing
pct_possible has 69794 (45.0%) missing values Missing
hr_precip is highly skewed (γ1 = 63.53586536) Skewed
snowfall_3hr is highly skewed (γ1 = 26.91591115) Skewed
snowfall_6hr is highly skewed (γ1 = 22.47496747) Skewed
hr_precip has 30738 (19.8%) zeros Zeros
snow_depth has 5364 (3.5%) zeros Zeros
snowfall_3hr has 55831 (36.0%) zeros Zeros
snowfall_6hr has 54240 (35.0%) zeros Zeros
snowfall_24hr has 51275 (33.0%) zeros Zeros
solar_radiation has 42637 (27.5%) zeros Zeros

Reproduction

Analysis started2023-01-06 16:02:20.418501
Analysis finished2023-01-06 16:02:53.875947
Duration33.46 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

dt
Date

Distinct70602
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Minimum2007-01-01 00:00:00
Maximum2021-12-31 23:00:00
2023-01-06T11:02:53.955000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:54.086684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temp
Real number (ℝ)

HIGH CORRELATION

Distinct144
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.41926024
Minimum-42
Maximum106
Zeros367
Zeros (%)0.2%
Memory size1.2 MiB
2023-01-06T11:02:54.205598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-42
5-th percentile9
Q123
median33
Q349
95-th percentile72
Maximum106
Range148
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.58854766
Coefficient of variation (CV)0.537862316
Kurtosis0.0766972782
Mean36.41926024
Median Absolute Deviation (MAD)12
Skewness0.2922759327
Sum5650776
Variance383.7111994
MonotocityNot monotonic
2023-01-06T11:02:54.321824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3243742.8%
 
3042302.7%
 
2840802.6%
 
3140462.6%
 
2739862.6%
 
2939852.6%
 
3338062.5%
 
2638022.5%
 
2535692.3%
 
2434352.2%
 
Other values (134)11584674.7%
 
ValueCountFrequency (%) 
-422< 0.1%
 
-416< 0.1%
 
-391< 0.1%
 
-382< 0.1%
 
-351< 0.1%
 
ValueCountFrequency (%) 
1061< 0.1%
 
1042< 0.1%
 
1021< 0.1%
 
1012< 0.1%
 
1005< 0.1%
 

dew_pt
Real number (ℝ)

MISSING

Distinct153
Distinct (%)0.1%
Missing18320
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean26.53768297
Minimum-76
Maximum101
Zeros400
Zeros (%)0.3%
Memory size1.2 MiB
2023-01-06T11:02:54.441510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-76
5-th percentile3
Q118
median27
Q337
95-th percentile49
Maximum101
Range177
Interquartile range (IQR)19

Descriptive statistics

Standard deviation15.34902156
Coefficient of variation (CV)0.5783858967
Kurtosis3.949791731
Mean26.53768297
Median Absolute Deviation (MAD)9
Skewness-1.019069054
Sum3631390
Variance235.5924629
MonotocityNot monotonic
2023-01-06T11:02:54.554727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2643752.8%
 
2842352.7%
 
3041882.7%
 
2441222.7%
 
2540722.6%
 
3240572.6%
 
2338772.5%
 
2037552.4%
 
2236672.4%
 
1936582.4%
 
Other values (143)9683362.4%
 
(Missing)1832011.8%
 
ValueCountFrequency (%) 
-762< 0.1%
 
-752< 0.1%
 
-744< 0.1%
 
-734< 0.1%
 
-7211< 0.1%
 
ValueCountFrequency (%) 
1011< 0.1%
 
751< 0.1%
 
745< 0.1%
 
736< 0.1%
 
726< 0.1%
 

rH
Real number (ℝ≥0)

MISSING

Distinct100
Distinct (%)0.1%
Missing18376
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean73.1108398
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2023-01-06T11:02:54.681646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q155
median81
Q393
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)38

Descriptive statistics

Standard deviation23.39028427
Coefficient of variation (CV)0.3199290876
Kurtosis-0.5048161741
Mean73.1108398
Median Absolute Deviation (MAD)15
Skewness-0.7702044401
Sum10000320
Variance547.1053981
MonotocityNot monotonic
2023-01-06T11:02:55.071040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10057253.7%
 
9452373.4%
 
9549513.2%
 
9649243.2%
 
9247763.1%
 
9344392.9%
 
9742542.7%
 
9139592.6%
 
9039002.5%
 
9837742.4%
 
Other values (90)9084458.5%
 
(Missing)1837611.8%
 
ValueCountFrequency (%) 
11490.1%
 
22250.1%
 
31830.1%
 
41070.1%
 
551< 0.1%
 
ValueCountFrequency (%) 
10057253.7%
 
9931302.0%
 
9837742.4%
 
9742542.7%
 
9649243.2%
 

heat_idx
Real number (ℝ≥0)

MISSING

Distinct29
Distinct (%)1.0%
Missing152201
Missing (%)98.1%
Infinite0
Infinite (%)0.0%
Mean82.83400947
Minimum78
Maximum201
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2023-01-06T11:02:55.172107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile79
Q180
median82
Q384
95-th percentile89
Maximum201
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.16856112
Coefficient of variation (CV)0.05032427075
Kurtosis220.3628771
Mean82.83400947
Median Absolute Deviation (MAD)2
Skewness8.828742129
Sum245023
Variance17.37690181
MonotocityNot monotonic
2023-01-06T11:02:55.279051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
805580.4%
 
814710.3%
 
823760.2%
 
793090.2%
 
832750.2%
 
842250.1%
 
851830.1%
 
861480.1%
 
871060.1%
 
88980.1%
 
Other values (19)2090.1%
 
(Missing)15220198.1%
 
ValueCountFrequency (%) 
7815< 0.1%
 
793090.2%
 
805580.4%
 
814710.3%
 
823760.2%
 
ValueCountFrequency (%) 
2011< 0.1%
 
1101< 0.1%
 
1071< 0.1%
 
1044< 0.1%
 
1022< 0.1%
 

wind_chill
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct111
Distinct (%)0.2%
Missing96191
Missing (%)62.0%
Infinite0
Infinite (%)0.0%
Mean16.10161443
Minimum-70
Maximum42
Zeros652
Zeros (%)0.4%
Memory size1.2 MiB
2023-01-06T11:02:55.393027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-70
5-th percentile-10
Q18
median18
Q326
95-th percentile37
Maximum42
Range112
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.06432757
Coefficient of variation (CV)0.9355787044
Kurtosis2.398416701
Mean16.10161443
Median Absolute Deviation (MAD)9
Skewness-1.127472988
Sum949480
Variance226.9339652
MonotocityNot monotonic
2023-01-06T11:02:55.505568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1621831.4%
 
1920851.3%
 
1819491.3%
 
2119361.2%
 
2218971.2%
 
1418831.2%
 
2417551.1%
 
1116381.1%
 
1016291.0%
 
2316091.0%
 
Other values (101)4040426.0%
 
(Missing)9619162.0%
 
ValueCountFrequency (%) 
-701< 0.1%
 
-691< 0.1%
 
-681< 0.1%
 
-671< 0.1%
 
-651< 0.1%
 
ValueCountFrequency (%) 
421100.1%
 
413530.2%
 
403600.2%
 
399160.6%
 
388560.6%
 

wind_dir
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing7972
Missing (%)5.1%
Memory size1.2 MiB
WSW
32999 
SW
26696 
SSW
14553 
NW
13111 
W
10370 
Other values (11)
49458 
ValueCountFrequency (%) 
WSW3299921.3%
 
SW2669617.2%
 
SSW145539.4%
 
NW131118.5%
 
W103706.7%
 
WNW95446.2%
 
NE58353.8%
 
S56013.6%
 
NNE53473.4%
 
N50363.2%
 
Other values (6)1809511.7%
 
(Missing)79725.1%
 
2023-01-06T11:02:55.614201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-06T11:02:55.707763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.372798226
Min length1

wind_spd
Categorical

HIGH CARDINALITY

Distinct2124
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0G0
 
4984
1G4
 
3514
1G3
 
3031
2G6
 
2399
1G5
 
2355
Other values (2119)
138876 
ValueCountFrequency (%) 
0G049843.2%
 
1G435142.3%
 
1G330312.0%
 
2G623991.5%
 
1G523551.5%
 
2G723461.5%
 
2G522091.4%
 
0G220171.3%
 
2G818991.2%
 
3G818401.2%
 
Other values (2114)12856582.9%
 
2023-01-06T11:02:55.830066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique660 ?
Unique (%)0.4%
2023-01-06T11:02:55.931514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length3.985808107
Min length1

hr_precip
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct49
Distinct (%)0.1%
Missing120618
Missing (%)77.7%
Infinite0
Infinite (%)0.0%
Mean0.007582003995
Minimum0
Maximum19.58
Zeros30738
Zeros (%)19.8%
Memory size1.2 MiB
2023-01-06T11:02:56.035020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.02
Maximum19.58
Range19.58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2425489193
Coefficient of variation (CV)31.99008065
Kurtosis4329.938167
Mean0.007582003995
Median Absolute Deviation (MAD)0
Skewness63.53586536
Sum261.89
Variance0.05882997826
MonotocityNot monotonic
2023-01-06T11:02:56.151263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%) 
03073819.8%
 
0.0117351.1%
 
0.027390.5%
 
0.033840.2%
 
0.042600.2%
 
0.051760.1%
 
0.061350.1%
 
0.0776< 0.1%
 
0.0857< 0.1%
 
0.0949< 0.1%
 
Other values (39)1920.1%
 
(Missing)12061877.7%
 
ValueCountFrequency (%) 
03073819.8%
 
0.0117351.1%
 
0.027390.5%
 
0.033840.2%
 
0.042600.2%
 
ValueCountFrequency (%) 
19.581< 0.1%
 
17.751< 0.1%
 
17.531< 0.1%
 
16.481< 0.1%
 
15.641< 0.1%
 

snow_depth
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1157
Distinct (%)1.3%
Missing68084
Missing (%)43.9%
Infinite0
Infinite (%)0.0%
Mean24.00705369
Minimum0
Maximum149.6
Zeros5364
Zeros (%)3.5%
Memory size1.2 MiB
2023-01-06T11:02:56.274239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.1
median12.2
Q335.7
95-th percentile77.9
Maximum149.6
Range149.6
Interquartile range (IQR)31.6

Descriptive statistics

Standard deviation26.58475153
Coefficient of variation (CV)1.107372519
Kurtosis1.635547209
Mean24.00705369
Median Absolute Deviation (MAD)10.8
Skewness1.409922921
Sum2090414.2
Variance706.7490138
MonotocityNot monotonic
2023-01-06T11:02:56.387045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
053643.5%
 
0.18730.6%
 
0.38470.5%
 
0.26920.4%
 
2.56130.4%
 
0.46090.4%
 
0.54540.3%
 
34280.3%
 
24250.3%
 
2.64170.3%
 
Other values (1147)7635349.2%
 
(Missing)6808443.9%
 
ValueCountFrequency (%) 
053643.5%
 
0.18730.6%
 
0.26920.4%
 
0.38470.5%
 
0.46090.4%
 
ValueCountFrequency (%) 
149.65< 0.1%
 
149.514< 0.1%
 
149.41< 0.1%
 
149.36< 0.1%
 
149.227< 0.1%
 

snowfall_3hr
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct341
Distinct (%)0.4%
Missing69198
Missing (%)44.6%
Infinite0
Infinite (%)0.0%
Mean0.5219925315
Minimum0
Maximum149.5
Zeros55831
Zeros (%)36.0%
Memory size1.2 MiB
2023-01-06T11:02:56.505269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile1.9
Maximum149.5
Range149.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation4.329892409
Coefficient of variation (CV)8.294931724
Kurtosis825.1669677
Mean0.5219925315
Median Absolute Deviation (MAD)0
Skewness26.91591115
Sum44871
Variance18.74796828
MonotocityNot monotonic
2023-01-06T11:02:56.621436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05583136.0%
 
0.155103.6%
 
0.238392.5%
 
0.327691.8%
 
0.423161.5%
 
0.517761.1%
 
0.614500.9%
 
0.712460.8%
 
0.811380.7%
 
0.98760.6%
 
Other values (331)92105.9%
 
(Missing)6919844.6%
 
ValueCountFrequency (%) 
05583136.0%
 
0.155103.6%
 
0.238392.5%
 
0.327691.8%
 
0.423161.5%
 
ValueCountFrequency (%) 
149.52< 0.1%
 
149.42< 0.1%
 
149.32< 0.1%
 
149.14< 0.1%
 
148.81< 0.1%
 

snowfall_6hr
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct380
Distinct (%)0.4%
Missing69547
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean0.7085466991
Minimum0
Maximum149.5
Zeros54240
Zeros (%)35.0%
Memory size1.2 MiB
2023-01-06T11:02:56.742826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.3
95-th percentile2.6
Maximum149.5
Range149.5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation5.451384955
Coefficient of variation (CV)7.693755348
Kurtosis560.0450366
Mean0.7085466991
Median Absolute Deviation (MAD)0
Skewness22.47496747
Sum60660.1
Variance29.71759793
MonotocityNot monotonic
2023-01-06T11:02:56.856638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05424035.0%
 
0.143402.8%
 
0.233052.1%
 
0.324781.6%
 
0.421731.4%
 
0.517191.1%
 
0.615201.0%
 
0.713350.9%
 
0.812010.8%
 
0.99960.6%
 
Other values (370)123057.9%
 
(Missing)6954744.8%
 
ValueCountFrequency (%) 
05424035.0%
 
0.143402.8%
 
0.233052.1%
 
0.324781.6%
 
0.421731.4%
 
ValueCountFrequency (%) 
149.52< 0.1%
 
149.41< 0.1%
 
149.35< 0.1%
 
149.11< 0.1%
 
148.91< 0.1%
 

snowfall_24hr
Real number (ℝ≥0)

MISSING
ZEROS

Distinct409
Distinct (%)0.5%
Missing69801
Missing (%)45.0%
Infinite0
Infinite (%)0.0%
Mean1.257981677
Minimum0
Maximum149.1
Zeros51275
Zeros (%)33.0%
Memory size1.2 MiB
2023-01-06T11:02:56.978531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.7
95-th percentile4.5
Maximum149.1
Range149.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation7.816349717
Coefficient of variation (CV)6.213405059
Kurtosis286.8508598
Mean1.257981677
Median Absolute Deviation (MAD)0
Skewness16.26845858
Sum107378.8
Variance61.0953229
MonotocityNot monotonic
2023-01-06T11:02:57.091470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05127533.0%
 
0.126521.7%
 
0.223511.5%
 
0.319971.3%
 
0.419211.2%
 
0.514961.0%
 
0.613780.9%
 
0.712640.8%
 
0.812040.8%
 
0.99730.6%
 
Other values (399)1884712.1%
 
(Missing)6980145.0%
 
ValueCountFrequency (%) 
05127533.0%
 
0.126521.7%
 
0.223511.5%
 
0.319971.3%
 
0.419211.2%
 
ValueCountFrequency (%) 
149.16< 0.1%
 
1492< 0.1%
 
148.84< 0.1%
 
148.73< 0.1%
 
148.631< 0.1%
 

site
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
SNSLP
68084 
JVEMT
17853 
S11MT
17818 
MRPMT
17424 
SH7MT
17291 
ValueCountFrequency (%) 
SNSLP6808443.9%
 
JVEMT1785311.5%
 
S11MT1781811.5%
 
MRPMT1742411.2%
 
SH7MT1729111.1%
 
SH4MT1668910.8%
 
2023-01-06T11:02:57.201286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-06T11:02:57.261277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:57.336852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

file_name
Categorical

HIGH CORRELATION

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
SNSLP-2016.html
 
8764
JVEMT-2020.html
 
8724
S11MT-2020.html
 
8710
SNSLP-2017.html
 
8558
MRPMT-2020.html
 
8362
Other values (23)
112041 
ValueCountFrequency (%) 
SNSLP-2016.html87645.6%
 
JVEMT-2020.html87245.6%
 
S11MT-2020.html87105.6%
 
SNSLP-2017.html85585.5%
 
MRPMT-2020.html83625.4%
 
SH7MT-2020.html83245.4%
 
SNSLP-2020.html78225.0%
 
SH4MT-2020.html77505.0%
 
SNSLP-2019.html75634.9%
 
SH4MT-2021.html73124.7%
 
Other values (18)7327047.2%
 
2023-01-06T11:02:57.438116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-06T11:02:57.531661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length15
Mean length15
Min length15

year
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2020
49692 
2021
42086 
2019
16560 
2016
8764 
2017
8558 
Other values (8)
29499 
ValueCountFrequency (%) 
20204969232.0%
 
20214208627.1%
 
20191656010.7%
 
201687645.6%
 
201785585.5%
 
201871394.6%
 
200862144.0%
 
200755913.6%
 
201435362.3%
 
201120401.3%
 
Other values (3)49793.2%
 
2023-01-06T11:02:57.619860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-06T11:02:57.706712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

sea_lvl_press
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct12847
Distinct (%)19.1%
Missing87974
Missing (%)56.7%
Infinite0
Infinite (%)0.0%
Mean962.4523845
Minimum762.09
Maximum1176.42
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2023-01-06T11:02:57.809431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum762.09
5-th percentile774.92
Q1956.99
median1011.07
Q31022.07
95-th percentile1073.31
Maximum1176.42
Range414.33
Interquartile range (IQR)65.08

Descriptive statistics

Standard deviation105.5316795
Coefficient of variation (CV)0.1096487278
Kurtosis-0.6366816303
Mean962.4523845
Median Absolute Deviation (MAD)15.32
Skewness-1.004717144
Sum64662363.45
Variance11136.93538
MonotocityNot monotonic
2023-01-06T11:02:57.916689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1015.46860.1%
 
1015.1272< 0.1%
 
1011.9268< 0.1%
 
101667< 0.1%
 
1015.860< 0.1%
 
1018.1659< 0.1%
 
1017.8959< 0.1%
 
1016.3458< 0.1%
 
101958< 0.1%
 
1012.2657< 0.1%
 
Other values (12837)6654142.9%
 
(Missing)8797456.7%
 
ValueCountFrequency (%) 
762.091< 0.1%
 
762.271< 0.1%
 
762.441< 0.1%
 
762.61< 0.1%
 
763.212< 0.1%
 
ValueCountFrequency (%) 
1176.421< 0.1%
 
1174.671< 0.1%
 
1173.971< 0.1%
 
1173.911< 0.1%
 
1173.031< 0.1%
 

sta_press
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct537
Distinct (%)0.6%
Missing70210
Missing (%)45.3%
Infinite0
Infinite (%)0.0%
Mean22.44570837
Minimum0
Maximum25.71
Zeros2
Zeros (%)< 0.1%
Memory size1.2 MiB
2023-01-06T11:02:58.026960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.77
Q122.73
median23.17
Q324.31
95-th percentile24.85
Maximum25.71
Range25.71
Interquartile range (IQR)1.58

Descriptive statistics

Standard deviation2.433299229
Coefficient of variation (CV)0.1084082172
Kurtosis-0.03609955493
Mean22.44570837
Median Absolute Deviation (MAD)1.02
Skewness-1.158871617
Sum1906740.48
Variance5.920945137
MonotocityNot monotonic
2023-01-06T11:02:58.145455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
23.1716081.0%
 
23.1415551.0%
 
23.113650.9%
 
23.213460.9%
 
23.0713380.9%
 
23.2412920.8%
 
23.2712830.8%
 
23.0412450.8%
 
17.8911520.7%
 
17.9210870.7%
 
Other values (527)7167846.2%
 
(Missing)7021045.3%
 
ValueCountFrequency (%) 
02< 0.1%
 
17.373< 0.1%
 
17.381< 0.1%
 
17.392< 0.1%
 
17.45< 0.1%
 
ValueCountFrequency (%) 
25.711< 0.1%
 
25.72< 0.1%
 
25.672< 0.1%
 
25.661< 0.1%
 
25.571< 0.1%
 

altimeter_setting
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct690
Distinct (%)0.8%
Missing70210
Missing (%)45.3%
Infinite0
Infinite (%)0.0%
Mean28.68547234
Minimum0
Maximum33.35
Zeros2
Zeros (%)< 0.1%
Memory size1.2 MiB
2023-01-06T11:02:58.261938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.05
Q129.44
median29.95
Q330.14
95-th percentile31.5
Maximum33.35
Range33.35
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation2.853638608
Coefficient of variation (CV)0.09948027261
Kurtosis0.2569864934
Mean28.68547234
Median Absolute Deviation (MAD)0.24
Skewness-1.336042908
Sum2436802.19
Variance8.143253308
MonotocityNot monotonic
2023-01-06T11:02:58.374781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
30.0212170.8%
 
30.0412100.8%
 
30.0612000.8%
 
30.0311860.8%
 
29.9911840.8%
 
30.0111260.7%
 
30.0511190.7%
 
3011060.7%
 
29.9710900.7%
 
30.0710700.7%
 
Other values (680)7344147.3%
 
(Missing)7021045.3%
 
ValueCountFrequency (%) 
02< 0.1%
 
22.533< 0.1%
 
22.551< 0.1%
 
22.562< 0.1%
 
22.575< 0.1%
 
ValueCountFrequency (%) 
33.351< 0.1%
 
33.332< 0.1%
 
33.32< 0.1%
 
33.291< 0.1%
 
33.171< 0.1%
 

solar_radiation
Real number (ℝ≥0)

MISSING
ZEROS

Distinct987
Distinct (%)1.2%
Missing69794
Missing (%)45.0%
Infinite0
Infinite (%)0.0%
Mean133.8078252
Minimum0
Maximum1067
Zeros42637
Zeros (%)27.5%
Memory size1.2 MiB
2023-01-06T11:02:58.495207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q3181
95-th percentile678
Maximum1067
Range1067
Interquartile range (IQR)181

Descriptive statistics

Standard deviation222.0364803
Coefficient of variation (CV)1.659368426
Kurtosis2.294663077
Mean133.8078252
Median Absolute Deviation (MAD)1
Skewness1.805046887
Sum11422505
Variance49300.19857
MonotocityNot monotonic
2023-01-06T11:02:58.607633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04263727.5%
 
23390.2%
 
33130.2%
 
52940.2%
 
12940.2%
 
42940.2%
 
62640.2%
 
112600.2%
 
122400.2%
 
82350.2%
 
Other values (977)4019525.9%
 
(Missing)6979445.0%
 
ValueCountFrequency (%) 
04263727.5%
 
12940.2%
 
23390.2%
 
33130.2%
 
42940.2%
 
ValueCountFrequency (%) 
10671< 0.1%
 
10561< 0.1%
 
10241< 0.1%
 
10202< 0.1%
 
10091< 0.1%
 

pct_possible
Categorical

HIGH CARDINALITY
MISSING

Distinct102
Distinct (%)0.1%
Missing69794
Missing (%)45.0%
Memory size1.2 MiB
--
42637 
100 %
 
1097
10 %
 
819
13 %
 
817
8 %
 
809
Other values (97)
39186 
ValueCountFrequency (%) 
--4263727.5%
 
100 %10970.7%
 
10 %8190.5%
 
13 %8170.5%
 
8 %8090.5%
 
5 %8010.5%
 
9 %7830.5%
 
11 %7760.5%
 
17 %7560.5%
 
7 %7510.5%
 
Other values (92)3531922.8%
 
(Missing)6979445.0%
 
2023-01-06T11:02:58.730080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-06T11:02:58.829461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length2.971583988
Min length2

Interactions

2023-01-06T11:02:36.519072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:36.603267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:36.675969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:36.746554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:36.817419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:36.894661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:36.973359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:37.053410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:37.129845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:37.209749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:37.288433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:37.374714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:37.464785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:37.740172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:37.827685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:37.904415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:37.981600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.059630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.142532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.221304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.301449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.374717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.447982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.515470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.588832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.657354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.731571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.802703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.871366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:38.939538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.007490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.076170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.156990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.232211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.300421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.370024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.457672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.535399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.620115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.694987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.761610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.830036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.903094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:39.980013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.051439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.118892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.186854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.256530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.323000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.389915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.597920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.681449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.750632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.823123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.894312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:40.965619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.033258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.101937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.169413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.234259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.300690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.367438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.433853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.501339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.568545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.636022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.702607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.769350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.835412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.902493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:41.970552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.046715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.115038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.181425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.247362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.314458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.382916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.451204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.523181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.593584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.664267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.744443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.824980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:42.936223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.018954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.094792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.170213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.242884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.322048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.397592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.468665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.536486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.603181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.671922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.741678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:43.811250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.044132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.135054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.216160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.297118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.372494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.446783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.525257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.607852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.684450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.765236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.843957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.923351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:44.998138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.070457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.146896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.227058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.306740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.379714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.460891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.538506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.608677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.681431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.757252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.835548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.915702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:45.994795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.073732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.160537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.245247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.326061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.396522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.463077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.531357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.600937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.671511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.742128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.809072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.876624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:46.942536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.010453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.077580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.144625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.210489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.278734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.346766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.415664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.483214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.548578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.615271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.682076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.749586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.816058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.884217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:47.951781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.019118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.088018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.363767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.447481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.515792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.583192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.648902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.715536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.782050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.850280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.918707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:48.986543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.054135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.121522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.191322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.258816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.325559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.392964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.461339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.528257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.595651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.661159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.728912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.798207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.865636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:49.933629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.003072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.071479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.140082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.208856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.276998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.345328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.415026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.482739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.549984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.616593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.683613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.751131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.820330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.887242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:50.955751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:51.024292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:51.107037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:51.190614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:51.259057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:51.327250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-06T11:02:58.919915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-06T11:02:59.082782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-06T11:02:59.240752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-06T11:02:59.406591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2023-01-06T11:02:59.558478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2023-01-06T11:02:51.703791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:52.338194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:53.236026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-06T11:02:53.646849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

dttempdew_ptrHheat_idxwind_chillwind_dirwind_spdhr_precipsnow_depthsnowfall_3hrsnowfall_6hrsnowfall_24hrsitefile_nameyearsea_lvl_presssta_pressaltimeter_settingsolar_radiationpct_possible
02021-12-31 23:00:00-12.0-16.080.0NaNNaNE2G50.018.90.00.00.0JVEMTJVEMT-2021.html2021NaNNaNNaNNaNNaN
12021-12-31 22:00:00-10.0-14.081.0NaNNaNE2G50.018.70.00.00.0JVEMTJVEMT-2021.html2021NaNNaNNaNNaNNaN
22021-12-31 21:00:00-7.0-11.082.0NaNNaNE2G50.019.10.00.10.0JVEMTJVEMT-2021.html2021NaNNaNNaNNaNNaN
32021-12-31 20:00:00-5.0-9.081.0NaNNaNE1G30.019.10.00.10.0JVEMTJVEMT-2021.html2021NaNNaNNaNNaNNaN
42021-12-31 19:00:00-2.0-7.079.0NaNNaNE1G30.019.10.10.10.0JVEMTJVEMT-2021.html2021NaNNaNNaNNaNNaN
52021-12-31 18:00:00-1.0-7.075.0NaNNaNE1G30.019.10.10.10.0JVEMTJVEMT-2021.html2021NaNNaNNaNNaNNaN
62021-12-31 17:00:000.0-7.072.0NaNNaNENE1G60.019.20.20.10.0JVEMTJVEMT-2021.html2021NaNNaNNaNNaNNaN
72021-12-31 16:00:001.0-10.058.0NaNNaNE5G110.019.00.00.00.0JVEMTJVEMT-2021.html2021NaNNaNNaNNaNNaN
82021-12-31 15:00:001.0-12.055.0NaN-12.0E7G150.019.00.00.00.0JVEMTJVEMT-2021.html2021NaNNaNNaNNaNNaN
92021-12-31 14:00:001.0-11.056.0NaN-12.0ESE7G140.019.00.00.00.5JVEMTJVEMT-2021.html2021NaNNaNNaNNaNNaN

Last rows

dttempdew_ptrHheat_idxwind_chillwind_dirwind_spdhr_precipsnow_depthsnowfall_3hrsnowfall_6hrsnowfall_24hrsitefile_nameyearsea_lvl_presssta_pressaltimeter_settingsolar_radiationpct_possible
1551492019-10-16 04:00:0048.0NaNNaNNaNNaNSW15G31NaN0.00.00.0NaNMRPMTMRPMT-2019.html2019NaN24.7029.94NaNNaN
1551502019-10-16 03:00:0048.0NaNNaNNaNNaNSW14G29NaN0.00.00.0NaNMRPMTMRPMT-2019.html2019NaN24.7029.95NaNNaN
1551512019-10-16 02:00:0049.0NaNNaNNaNNaNSW14G29NaN0.00.00.0NaNMRPMTMRPMT-2019.html2019NaN24.7029.94NaNNaN
1551522019-10-16 01:00:0049.0NaNNaNNaNNaNSW15G31NaN0.00.00.0NaNMRPMTMRPMT-2019.html2019NaN24.7029.94NaNNaN
1551532019-10-16 00:00:0049.0NaNNaNNaNNaNSW13G30NaN0.00.00.0NaNMRPMTMRPMT-2019.html2019NaN24.7029.94NaNNaN
1551542019-10-15 23:00:0049.0NaNNaNNaNNaNSW14G31NaN0.00.00.0NaNMRPMTMRPMT-2019.html2019NaN24.6929.93NaNNaN
1551552019-10-15 22:00:0049.0NaNNaNNaNNaNSW13G29NaN0.00.00.0NaNMRPMTMRPMT-2019.html2019NaN24.7029.95NaNNaN
1551562019-10-15 21:00:0048.0NaNNaNNaNNaNSW10G25NaN0.00.00.0NaNMRPMTMRPMT-2019.html2019NaN24.7229.97NaNNaN
1551572019-10-15 20:00:0048.0NaNNaNNaNNaNSSW10G19NaN0.00.00.0NaNMRPMTMRPMT-2019.html2019NaN24.7429.99NaNNaN
1551582019-10-15 19:00:0048.0NaNNaNNaNNaNSW10G20NaN0.00.00.0NaNMRPMTMRPMT-2019.html2019NaN24.7530.01NaNNaN